Survey On Plants Disease Detection Using Machine Learning

  IJETT-book-cover  International Journal of Recent Engineering Science (IJRES)          
© 2020 by IJRES Journal
Volume-7 Issue-2
Year of Publication : 2020
Authors : Preetha S, Musqan Arshad


MLA Style: Preetha S, Musqan Arshad "Survey On Plants Disease Detection Using Machine Learning" International Journal of Recent Engineering Science 7.2(2020):27-29. 

APA Style: Preetha S, Musqan Arshad. Survey On Plants Disease Detection Using Machine Learning  International Journal of Recent Engineering Science, 7(2),27-29.

Agriculture is a significant source of income for Indian people. Experts do the manual method of detecting disease in a plant. For this, a large team was required, and continuous monitoring was required; that was a complicated task when we do this with a large number of crops. In some places, farmers were unaware of the experts, and they do not have proper facilities. In such conditions, one technique can be beneficial in keeping track of and monitoring a large number of crops. This technique is known as Automatic Detection. This technique makes it much easier and cheaper to detect disease. Machine Learning can provide a method and algorithm to detect the disease. There should be the training of images of all types of leaves that include the ones that are healthy and disease leaf images

[1] Badage, Anuradha. “Crop Disease Detection using Machine Learning: Indian Agriculture.” IRJETV (2018).
[2] Khirade, Sachin D., and A. B. Patil. “Plant disease detection using image processing.” 2015 International conference on computing communication control and automation. IEEE, 2015.
[3] Prajapati, Bhumika S., Vipul K. Dabhi, and Harshadkumar B. Prajapati. “A survey on detection and classification of cotton leaf diseases.” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016.
[4] Ranjan, Malvika, et al. “Detection and classification of leaf disease using artificial neural network.” International Journal of Technical Research and Applications (IJTRA) 3.3 (2015).
[5] Revathi, P., and M. Hemalatha. “Advance computing enrichment evaluation of cotton leaf spot disease detection using Image Edge detection.” 2012 Third International Conference on Computing, Communication, and Networking Technologies (ICCCNT’12). IEEE, 2012.
[6] Sethupathy, Jayaprakash, and S. Veni. “Opencv based disease identification of mango leaves.” International Journal of Engineering and Technology 8.5 (2016): 1990-1998.
[7] Mohanty, Sharada P., David P. Hughes, and Marcel Salathé. “Using deep learning for image-based plant disease detection.” Frontiers in plant science 7 (2016): 1419.
[8] Madhogaria, Satish, et al. “Pixel-based classification method for detecting unhealthy regions in leaf images.” GI-Jahrestagung. 2011.
[9] Reddy, K. Narsimha, B. Polaiah, and N. Madhu. “A Literature Survey: Plant Leaf Diseases Detection Using Image Processing Techniques.” IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834.
[10] Malathi, M., et al. “A Survey on Plant Leaf Disease Detection Using Image Processing Techniques.” International Research Journal of Engineering and Technology (IRJET) 2.09 (2015).
[11] Wu, Stephen Gang, et al. “A leaf recognition algorithm for plant classification using probabilistic neural network.” 2007 IEEE international symposium on signal processing and information technology. IEEE, 2007.
[12] Rastogi, Aakanksha, Ritika Arora, and Shanu Sharma. “Leaf disease detection and grading using computer vision technology & fuzzy logic.” 2015 2nd international conference on signal processing and integrated networks (SPIN). IEEE, 2015.
[13] Maniyath, Shima Ramesh, et al. “Plant disease detection using machine learning.” 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C). IEEE, 2018.
[14] Rothe, P. R., and R. V. Kshirsagar. “Cotton leaf disease identification using pattern recognition techniques.” 2015 International Conference on Pervasive Computing (ICPC). IEEE, 2015.
[15] Dhakate, Mrunmayee, and A. B. Ingole. “Diagnosis of pomegranate plant diseases using a neural network.” 2015 fifth national conference on computer vision, pattern recognition, image processing, and graphics (NCVPRIPG). IEEE, 2015.
[16] T.Nagarathinam, Dr. K. Rameshkumar, "A Survey on Cluster Analysis Techniques for Plant Disease Diagnosis" SSRG International Journal of Computer Science and Engineering 3.6 (2019): 11-17 .
[17] Ashwin Dhakal, Prof. Dr. Subarna Shakya "Image-Based Plant Disease Detection with Deep Learning" International Journal of Computer Trends and Technology 61.1 (2018): 26-29.

Segmentation, Image acquisition, Feature extraction